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This work explores a distributed computing setting where $K$ nodes are
assigned fractions (subtasks) of a computational task in order to perform the
computation in parallel. In this setting, a well-known main bottleneck has been
the inter-node communication cost required to parallelize the task, because
unlike the computational cost which could keep decreasing as $K$ increases, the
communication cost remains approximately constant, thus bounding the total
speedup gains associated to having more computing nodes. This bottleneck was
substantially ameliorated by the recent introduction of coded MapReduce
techniques which allowed each node --- at the computational cost of having to
preprocess approximately $t$ times more subtasks --- to reduce its
communication cost by approximately $t$ times. In reality though, the
associated speed up gains were severely limited by the requirement that larger
$t$ and $K$ necessitated that the original task be divided into an extremely
large number of subt查看全文